Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients

TL;DR

This paper develops a method to derive policy rules from observational data for multi-action decision problems, applied here to optimize Hepatitis C treatment for HIV co-infected patients, improving outcomes and reducing costs.

Contribution

It introduces a general approach to estimate policy rules from observational data using weighted K-means and decision trees under weak assumptions, specifically applied to HIV/HCV treatment decisions.

Findings

Identified a subgroup with 80% chance of spontaneous HCV clearance without treatment.

Reallocating treatments could save CAN$3.6-4.9 million while increasing health benefits.

Method produces data-driven treatment guidelines for HIV/HCV co-infected patients.

Abstract

Decision-makers frequently must choose a single action from a finite set of alternatives -- for example, physicians selecting a treatment, investors choosing a portfolio risk level, or judges determining sentences. To improve outcomes, policymakers often issue policy rules or guidelines to inform such choices. In this paper, I show how to generally derive policy rules from observational data in a multi-action framework under relatively weak assumptions about the underlying structure of the heterogeneous sampled population. Conditional average treatment effects (CATEs) are consistently estimated via a weighted K-means algorithm, assuming the outcome model is correctly specified within each homogeneous subgroup. Feasible policy rules are then implemented via a standard decision tree, allowing for both perfect and imperfect adherence to treatment. The methodology is applied to treatment options for Hepatitis C (HCV) among patients co-infected with human immunodeficiency virus (HIV), a setting in which no uniform guideline exists for modern pharmaceutical therapies. The results identify a subgroup of patients with approximately an 80% probability of spontaneous HCV clearance without treatment. Estimation results also show that reallocating treatments among treated individuals could have reduced total treatment costs by CAN$3.6-4.9 million while still increasing aggregate health benefits relative to the status quo. These findings demonstrate that the proposed approach can generate improved, data-driven treatment guidelines for the management of HIV/HCV co-infected patients.